---
tags: [semantic-segmentation, remote-sensing, unet, resnet18, geonusaf]
library_name: segmentation-models-pytorch
---

# GeoNUSAF - UNet-ResNet18 - random split, fold 1

Kathmandu Valley land-use segmentation, 6 classes, ignore_index=255.
Weights are the **EMA** weights (decay 0.999), not the raw final weights.

| field | value |
|---|---|
| architecture | smp.Unet, encoder resnet18 (ImageNet), decoder [128, 64, 32, 16, 8] |
| params | 12.46 M |
| arch version | unet-r18-v1 |
| split mode | random |
| fold | 1 of 3 |
| seed | 42 |
| input | 512x512, ImageNet norm, effective GSD 0.586 m/px |
| regularization | wd 0.01 (norm/bias exempt), ls 0.05, drop 0.1, EMA 0.999 |
| classes | Residential, Road, River, Forest, UnusedLand, Agricultural |
| best epoch | 55 |
| val mIoU | 0.4465 |
| val mF1 | 0.5887 |
| val OA | 0.7561 |
| val kappa | 0.6138 |

## Per-class (validation)
| class | IoU | F1 |
|---|---|---|
| Residential | 0.8136 | 0.8973 |
| Road | 0.3643 | 0.5341 |
| River | 0.1713 | 0.2924 |
| Forest | 0.6048 | 0.7537 |
| UnusedLand | 0.2840 | 0.4424 |
| Agricultural | 0.4412 | 0.6122 |

Checkpoint `best.pt` holds `model_state` (EMA) plus `cfg`, `metrics` and `arch_sig`.